5 citations · 5 across the 4 of their papers we have counts for
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cs.LG2026
PhaseQuant: Mitigating Activation Quantization Collapse via Residual-Stream Dynamics
Zimo Zhao, Maolin Wang, Bowen Yu +3
Post-training quantization (PTQ) is essential for efficient large language model (LLM) inference, but reliably quantizing activations remains challenging when weights, activations,…
cs.CV2026
Beyond Language: Grounding Referring Expressions with Hand Pointing in Egocentric Vision
Ling Li, Bowen Liu, Zinuo Zhan +4
Traditional Visual Grounding (VG) predominantly relies on textual descriptions to localize objects, a paradigm that inherently struggles with linguistic ambiguity and often ignores…
cs.CV2026
How to Utilize Complementary Vision-Text Information for 2D Structure Understanding
Jiancheng Dong, Pengyue Jia, Derong Xu +9
LLMs typically linearize 2D tables into 1D sequences to fit their autoregressive architecture, which weakens row-column adjacency and other layout cues. In contrast, purely visual…